Table REF  shows the performance obtained over the contaminated dataset. Focusing specifically on 1D-CNN performance, the following is observed:
a) its per-subject classification accuracy is sensibly higher than the one with correct data (compare first row in Table REF  with fifth row of top Table REF );
b) its per-subject classification accuracy on the contaminated scenario resembles the one reported in [1]} (compare first row in Table REF  with second row of bottom Table REF );
c) in case of huge bias, as the one introduced with the contaminated scenario, pooling data significantly decreases its overall performance (from about 66.8% to 8.7% on block design and from 47.2% to 3% on the rapid design, shown in the first row of Table REF ).
Moreover, it is important to note that, with the data affected by strong temporal correlation as in our contaminated scenario, pooling data from multiple subjects affects more the accuracy of 1D-CNN method [1]} than the one of [3]}. We argue this depends on the models' architecture.
Indeed,
1D-CNN [1]},
unlike [3]}, employs channel-wise 1D convolutions at the initial layers. Allowing the network to focus on each channel independently makes it easier to capture spurious discriminative dynamics, compared to the case when all channels are processed from the first layer of the model. This means that if, for instance, only one channel of one subject shows specific temporal correlation in EEG data, classification results for that subject will be higher and, in case of per-subject analysis, this will affect average classification accuracy.
When pooling data, 1D-CNN [1]}, instead, cannot use single-channel specific dynamics and this explains the significant drop in performance reported in Table REF .
A similar behavior was also observed for SyncNet  on the contaminated scenario: its performance drops from 67.4% with per-subject analysis to 30.3% with pooled data on block-design and, from 41.2% to 11.8% on the rapid design as shown in Table REF .
Similar to 1D-CNN [1]}, SyncNet  performs channel-wise 1D convolutions at the initial layers. The other models , [8]}, instead, show a behavior similar to [3]} (i.e., performance with pooled data higher than, or similar to, that with per-subject data, see Table REF ) and are all characterized by having convolutions processing all EEG channels together since the first layer; naturally,
given the strong induced temporal correlation, those methods are able to achieve good results even when pooling data.
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